Changeset 11144 for stable/HeuristicLab.Problems.DataAnalysis/3.4/Implementation

Timestamp:

07/08/14 19:03:36 (10 years ago)

Author:

mkommend

Message:

#1758: Merged r10173:10176 and r10540, r10541, r10543, r10545 and r11031 into stable.

Location:

stable

Files:

• . (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationProblemData.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/DataAnalysisProblemData.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionProblemData.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisProblemData.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs (modified) (3 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs (modified) (2 diffs)

stable/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationProblemData.cs

@@ -221 +221 @@
     public string TargetVariable {
       get { return TargetVariableParameter.Value.Value; }
+      set {
+        if (value == null) throw new ArgumentNullException("targetVariable", "The provided value for the targetVariable is null.");
+        if (value == TargetVariable) return;
+
+
+        var matchingParameterValue = TargetVariableParameter.ValidValues.FirstOrDefault(v => v.Value == value);
+        if (matchingParameterValue == null) throw new ArgumentException("The provided value is not valid as the targetVariable.", "targetVariable");
+        TargetVariableParameter.Value = matchingParameterValue;
+      }
     }
 
@@ -408 +417 @@
     }
     #endregion
+
+    protected override bool IsProblemDataCompatible(IDataAnalysisProblemData problemData, out string errorMessage) {
+      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
+      IClassificationProblemData classificationProblemData = problemData as IClassificationProblemData;
+      if (classificationProblemData == null)
+        throw new ArgumentException("The problem data is no classification problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
+
+      var returnValue = base.IsProblemDataCompatible(classificationProblemData, out errorMessage);
+      //check targetVariable
+      if (classificationProblemData.InputVariables.All(var => var.Value != TargetVariable)) {
+        errorMessage = string.Format("The target variable {0} is not present in the new problem data.", TargetVariable)
+                       + Environment.NewLine + errorMessage;
+        return false;
+      }
+
+      var newClassValues = classificationProblemData.Dataset.GetDoubleValues(TargetVariable).Distinct().OrderBy(x => x);
+      if (!newClassValues.SequenceEqual(ClassValues)) {
+        errorMessage = errorMessage + string.Format("The class values differ in the provided classification problem data.");
+        return false;
+      }
+
+      return returnValue;
+    }
+
+    public override void AdjustProblemDataProperties(IDataAnalysisProblemData problemData) {
+      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
+      ClassificationProblemData classificationProblemData = problemData as ClassificationProblemData;
+      if (classificationProblemData == null)
+        throw new ArgumentException("The problem data is not a classification problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
+
+      base.AdjustProblemDataProperties(problemData);
+      TargetVariable = classificationProblemData.TargetVariable;
+      for (int i = 0; i < classificationProblemData.ClassNames.Count(); i++)
+        ClassNamesParameter.Value[i, 0] = classificationProblemData.ClassNames.ElementAt(i);
+
+      for (int i = 0; i < Classes; i++) {
+        for (int j = 0; j < Classes; j++) {
+          ClassificationPenaltiesParameter.Value[i, j] = classificationProblemData.GetClassificationPenalty(ClassValuesCache[i], ClassValuesCache[j]);
+        }
+      }
+    }
   }
 }

stable/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/DataAnalysisProblemData.cs

@@ -23 +23 @@
 using System.Collections.Generic;
 using System.Linq;
+using System.Text;
 using HeuristicLab.Collections;
 using HeuristicLab.Common;
@@ -158 +159 @@
       if (listeners != null) listeners(this, EventArgs.Empty);
     }
+
+    protected virtual bool IsProblemDataCompatible(IDataAnalysisProblemData problemData, out string errorMessage) {
+      errorMessage = string.Empty;
+      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
+
+      //check allowed input variables
+      StringBuilder message = new StringBuilder();
+      var variables = new HashSet<string>(problemData.InputVariables.Select(x => x.Value));
+      foreach (var item in AllowedInputVariables) {
+        if (!variables.Contains(item))
+          message.AppendLine("Input variable '" + item + "' is not present in the new problem data.");
+      }
+
+      if (message.Length != 0) {
+        errorMessage = message.ToString();
+        return false;
+      }
+      return true;
+
+    }
+
+    public virtual void AdjustProblemDataProperties(IDataAnalysisProblemData problemData) {
+      DataAnalysisProblemData data = problemData as DataAnalysisProblemData;
+      if (data == null) throw new ArgumentException("The problem data is not a data analysis problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
+
+      string errorMessage;
+      if (!data.IsProblemDataCompatible(this, out errorMessage)) {
+        throw new InvalidOperationException(errorMessage);
+      }
+
+      foreach (var inputVariable in InputVariables) {
+        var variable = data.InputVariables.FirstOrDefault(i => i.Value == inputVariable.Value);
+        InputVariables.SetItemCheckedState(inputVariable, variable != null && data.InputVariables.ItemChecked(variable));
+      }
+
+      TrainingPartition.Start = TrainingPartition.End = 0;
+      TestPartition.Start = 0;
+      TestPartition.End = Dataset.Rows;
+    }
   }
 }

stable/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionProblemData.cs

@@ -100 +100 @@
     public string TargetVariable {
       get { return TargetVariableParameter.Value.Value; }
+      set {
+        if (value == null) throw new ArgumentNullException("targetVariable", "The provided value for the targetVariable is null.");
+        if (value == TargetVariable) return;
+
+        var matchingParameterValue = TargetVariableParameter.ValidValues.FirstOrDefault(v => v.Value == value);
+        if (matchingParameterValue == null) throw new ArgumentException("The provided value is not valid as the targetVariable.", "targetVariable");
+        TargetVariableParameter.Value = matchingParameterValue;
+      }
     }
 
@@ -142 +150 @@
       OnChanged();
     }
+
+    protected override bool IsProblemDataCompatible(IDataAnalysisProblemData problemData, out string errorMessage) {
+      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
+      IRegressionProblemData regressionProblemData = problemData as IRegressionProblemData;
+      if (regressionProblemData == null)
+        throw new ArgumentException("The problem data is not a regression problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
+
+      var returnValue = base.IsProblemDataCompatible(problemData, out errorMessage);
+      //check targetVariable
+      if (problemData.InputVariables.All(var => var.Value != TargetVariable)) {
+        errorMessage = string.Format("The target variable {0} is not present in the new problem data.", TargetVariable)
+                       + Environment.NewLine + errorMessage;
+        return false;
+      }
+      return returnValue;
+    }
+
+    public override void AdjustProblemDataProperties(IDataAnalysisProblemData problemData) {
+      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
+      RegressionProblemData regressionProblemData = problemData as RegressionProblemData;
+      if (regressionProblemData == null)
+        throw new ArgumentException("The problem data is not a regression problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
+
+      base.AdjustProblemDataProperties(problemData);
+      TargetVariable = regressionProblemData.TargetVariable;
+    }
   }
 }

stable/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisProblemData.cs

@@ -1621 +1621 @@
     }
 
+    protected override bool IsProblemDataCompatible(IDataAnalysisProblemData problemData, out string errorMessage) {
+      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
+      ITimeSeriesPrognosisProblemData timeseriesProblemData = problemData as ITimeSeriesPrognosisProblemData;
+      if (timeseriesProblemData == null)
+        throw new ArgumentException("The problem data is not a time-series problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
+
+      var returnValue = base.IsProblemDataCompatible(problemData, out errorMessage);
+      //check targetVariable
+      if (problemData.InputVariables.All(var => var.Value != TargetVariable)) {
+        errorMessage = string.Format("The target variable {0} is not present in the new problem data.", TargetVariable)
+                       + Environment.NewLine + errorMessage;
+        return false;
+      }
+      return returnValue;
+    }
+
+    public override void AdjustProblemDataProperties(IDataAnalysisProblemData problemData) {
+      TimeSeriesPrognosisProblemData timeSeriesProblemData = problemData as TimeSeriesPrognosisProblemData;
+      if (timeSeriesProblemData == null)
+        throw new ArgumentException("The problem data is not a timeseries problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
+
+      var trainingDataStart = TrainingIndices.First();
+      
+      base.AdjustProblemDataProperties(problemData);
+      
+      TestPartition.Start = trainingDataStart;
+
+      TrainingHorizon = timeSeriesProblemData.TrainingHorizon;
+      TestHorizon = timeSeriesProblemData.TestHorizon;
+    }
+
   }
 }

stable/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs

@@ -369 +369 @@
       OnlineCalculatorError errorState;
       var problemData = Solution.ProblemData;
+      if (!problemData.TrainingIndices.Any()) return;
       var model = Solution.Model;
       //mean model
@@ -415 +416 @@
       OnlineCalculatorError errorState;
       var problemData = Solution.ProblemData;
+      if (!problemData.TestIndices.Any()) return;
       var model = Solution.Model;
-      //mean model
-      double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
-      var meanModel = new ConstantTimeSeriesPrognosisModel(trainingMean);
-
-      //AR1 model
-      double alpha, beta;
-      IEnumerable<double> trainingStartValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
-      OnlineLinearScalingParameterCalculator.Calculate(problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
-      var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(problemData.TargetVariable, new double[] { beta }, alpha);
-
       var testHorizions = problemData.TestIndices.Select(r => Math.Min(testHorizon, problemData.TestPartition.End - r)).ToList();
       IEnumerable<IEnumerable<double>> testTargetValues = problemData.TestIndices.Zip(testHorizions, Enumerable.Range).Select(r => problemData.Dataset.GetDoubleValues(problemData.TargetVariable, r)).ToList();
       IEnumerable<IEnumerable<double>> testEstimatedValues = model.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
       IEnumerable<double> testStartValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TestIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
-      IEnumerable<IEnumerable<double>> testMeanModelPredictions = meanModel.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
-      IEnumerable<IEnumerable<double>> testAR1ModelPredictions = AR1model.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
 
       IEnumerable<double> originalTestValues = testTargetValues.SelectMany(x => x).ToList();
@@ -453 +443 @@
       PrognosisTestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testStartValues, testTargetValues, testEstimatedValues, out errorState);
       PrognosisTestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTestWeightedDirectionalSymmetry : 0.0;
-      PrognosisTestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
-      PrognosisTestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticAR1 : double.PositiveInfinity;
-      PrognosisTestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
-      PrognosisTestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticMean : double.PositiveInfinity;
+
+
+      if (problemData.TrainingIndices.Any()) {
+        //mean model
+        double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
+        var meanModel = new ConstantTimeSeriesPrognosisModel(trainingMean);
+
+        //AR1 model
+        double alpha, beta;
+        IEnumerable<double> trainingStartValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
+        OnlineLinearScalingParameterCalculator.Calculate(problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
+        var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(problemData.TargetVariable, new double[] { beta }, alpha);
+
+        IEnumerable<IEnumerable<double>> testMeanModelPredictions = meanModel.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
+        IEnumerable<IEnumerable<double>> testAR1ModelPredictions = AR1model.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
+
+        PrognosisTestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
+        PrognosisTestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticAR1 : double.PositiveInfinity;
+        PrognosisTestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
+        PrognosisTestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticMean : double.PositiveInfinity;
+      }
     }
   }

stable/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs

@@ -149 +149 @@
     protected void CalculateTimeSeriesResults() {
       OnlineCalculatorError errorState;
-      double trainingMean = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).Average();
+      double trainingMean = ProblemData.TrainingIndices.Any() ? ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).Average() : double.NaN;
       var meanModel = new ConstantTimeSeriesPrognosisModel(trainingMean);
 
@@ -159 +159 @@
 
       #region Calculate training quality measures
-      IEnumerable<double> trainingTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
-      IEnumerable<double> trainingEstimatedValues = EstimatedTrainingValues.ToList();
-      IEnumerable<double> trainingMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList();
-      IEnumerable<double> trainingAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList();
-
-      TrainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState);
-      TrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingDirectionalSymmetry : 0.0;
-      TrainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState);
-      TrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingWeightedDirectionalSymmetry : 0.0;
-      TrainingTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingAR1ModelPredictions, trainingEstimatedValues, out errorState);
-      TrainingTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticAR1 : double.PositiveInfinity;
-      TrainingTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingMeanModelPredictions, trainingEstimatedValues, out errorState);
-      TrainingTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticMean : double.PositiveInfinity;
+      if (ProblemData.TrainingIndices.Any()) {
+        IEnumerable<double> trainingTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
+        IEnumerable<double> trainingEstimatedValues = EstimatedTrainingValues.ToList();
+        IEnumerable<double> trainingMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList();
+        IEnumerable<double> trainingAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList();
+
+        TrainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState);
+        TrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingDirectionalSymmetry : 0.0;
+        TrainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState);
+        TrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingWeightedDirectionalSymmetry : 0.0;
+        TrainingTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingAR1ModelPredictions, trainingEstimatedValues, out errorState);
+        TrainingTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticAR1 : double.PositiveInfinity;
+        TrainingTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingMeanModelPredictions, trainingEstimatedValues, out errorState);
+        TrainingTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticMean : double.PositiveInfinity;
+      }
       #endregion
 
       #region Calculate test quality measures
-      IEnumerable<double> testTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToList();
-      IEnumerable<double> testEstimatedValues = EstimatedTestValues.ToList();
-      IEnumerable<double> testMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList();
-      IEnumerable<double> testAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList();
-
-      TestDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState);
-      TestDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestDirectionalSymmetry : 0.0;
-      TestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState);
-      TestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestWeightedDirectionalSymmetry : 0.0;
-      TestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
-      TestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticAR1 : double.PositiveInfinity;
-      TestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
-      TestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticMean : double.PositiveInfinity;
+      if (ProblemData.TestIndices.Any()) {
+        IEnumerable<double> testTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToList();
+        IEnumerable<double> testEstimatedValues = EstimatedTestValues.ToList();
+        IEnumerable<double> testMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList();
+        IEnumerable<double> testAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList();
+
+        TestDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState);
+        TestDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestDirectionalSymmetry : 0.0;
+        TestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState);
+        TestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestWeightedDirectionalSymmetry : 0.0;
+        TestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
+        TestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticAR1 : double.PositiveInfinity;
+        TestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
+        TestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticMean : double.PositiveInfinity;
+      }
       #endregion
     }